等效智能体制导用于协同无人机载荷运输

中文摘要 AI 辅助

本文开发了一种制导框架，用于由两架无人驾驶飞行器（UAV）协同将刚性载荷运输至静止和机动着陆平台。首先引入虚拟等效智能体表示来描述刚性耦合的无人机-载荷系统的平移运动，从而将运输问题表述为相对于着陆平台的相对距离和视线动力学。几何分析确立了载荷交付的终端可行性条件。特别地，对于静止平台可以实现任意指定的接近角，而对于具有零相对速度的机动平台的成功交付需要终端速度和航向角同步，因此着陆角为零。利用该框架，开发了一种鲁棒的固定时间滑模制导策略来调节相对距离和视线动力学。提出了一种独立的连杆定向控制器和控制分配方案，以将虚拟等效智能体命令映射到单个无人机的控制输入。所提出的策略保证在一致有界时间内收敛到期望的着陆配置，与初始交战几何无关，同时明确适应目标机动引起的不确定性。数值模拟展示了在不同终端接近角和平台机动下的准确交付，而在树莓派上的处理器在环实现表明该制导算法满足实时计算要求。尽管如此，比较分析表明，所提出的框架实现了更好的跟踪精度和更快的滑模面收敛，同时每个无人机所需的控制能量显著减少。

英文摘要

This paper develops a guidance framework for cooperative transportation of a rigid payload by two uncrewed aerial vehicles (UAVs) to stationary and maneuvering landing platforms. A virtual equivalent-agent representation is first introduced to describe the translational motion of the rigidly coupled UAV-payload system, allowing the transportation problem to be formulated in terms of relative range and line-of-sight dynamics with respect to the landing platform. A geometric analysis establishes the terminal feasibility conditions for payload delivery. In particular, an arbitrary prescribed approach angle can be achieved for a stationary platform, whereas successful delivery to a maneuvering platform with zero relative velocity requires terminal velocity and heading angle synchronization and consequently a zero landing angle. Leveraging this framework, a robust fixed-time sliding mode guidance strategy is developed to regulate both relative range and line-of-sight dynamics. A separate link-orientation controller and control allocation scheme is presented to map virtual equivalent agent commands to the individual UAV's control inputs. The proposed strategy guarantees convergence to the desired landing configuration within a uniformly bounded time, independent of initial engagement geometries, while explicitly accommodating uncertainties arising from target maneuvers. Numerical simulations demonstrate accurate delivery under different terminal approach angles and platform maneuvers, while processor-in-the-loop implementation on a Raspberry Pi demonstrates that the guidance algorithm satisfies the real-time computational requirements. Nonetheless, a comparative analysis shows that the proposed framework achieves better tracking accuracy and faster sliding surface convergence while requiring significantly less control energy from each UAV.

发表机构

• Indian Institute of Technology Bombay（印度理工学院孟买分校）
• University of Cincinnati（辛辛那提大学）

机构由 AI 辅助整理，请以论文原文为准。